An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting

Ceylan, Uğur
The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of collaborative filtering. The content-based part of the proposed approach exploits semantic similarities between items based on a priori defined ontology-based metadata in movie domain and derived feature-weights from content-based user models. Using the semantic similarities between items and collaborative-based user models, recommendations are generated. The results of the evaluation phase show that the proposed approach improves the quality of recommendations.


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Citation Formats
U. Ceylan, “An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting,” M.S. - Master of Science, Middle East Technical University, 2011.